scholarly journals A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yosra Yousif ◽  
Faiz A. M. Elfaki ◽  
Meftah Hrairi ◽  
Oyelola A. Adegboye
Keyword(s):  
Competing Risks ◽  
Bayesian Approach ◽  
Survival Data ◽  
Failure Time ◽  
Production Engineering ◽  
Hazard Functions ◽  
Interval Censored ◽  
Gamma Processes ◽  
The Impact ◽  
Causes Of Failure

We present a Bayesian approach for analysis of competing risks survival data with masked causes of failure. This approach is often used to assess the impact of covariates on the hazard functions when the failure time is exactly observed for some subjects but only known to lie in an interval of time for the remaining subjects. Such data, known as partly interval-censored data, usually result from periodic inspection in production engineering. In this study, Dirichlet and Gamma processes are assumed as priors for masking probabilities and baseline hazards. Markov chain Monte Carlo (MCMC) technique is employed for the implementation of the Bayesian approach. The effectiveness of the proposed approach is illustrated with simulated and production engineering applications.

scholarly journals Bayesian Analysis of Competing Risks Models with Masked Causes of Failure and Incomplete Failure Times

2017 ◽  
Author(s):  
Yosra Yousif ◽  
Faiz A. M. Elfaki ◽  
Meftah Hrairi
Keyword(s):  
Bayesian Analysis ◽  
Failure Time ◽  
Real Data ◽  
Data Sets ◽  
Hazard Functions ◽  
Periodic Inspection ◽  
Proposed Model ◽  
Interval Censored ◽  
Gamma Processes ◽  
The Impact

Bayesian analysis for masked data under competing risk frameworks is studied for the purpose of assessing the impact of covariates on the hazard functions when the failure time is exactly observed for some subjects but only known to lie in an interval of time for the remaining subjects. Such data, known as partly interval-censored data, usually result from periodic inspection. Dirichlet and Gamma processes are assumed as priors for masking probabilities and baseline hazards. The Markov Chain Monte Carlo (MCMC) technique is employed for the implementation of the Bayesian approach. The effectiveness of the proposed model is tested through numerical studies, including simulated and real data sets.

An issue of identifying longitudinal biomarkers for competing risks data with masked causes of failure considering frailties model

2019 ◽  
Vol 29 (2) ◽  
pp. 603-616
Author(s):  
Feng-shou Ko
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Score Test ◽  
Joint Modeling ◽  
Repeated Measurements ◽  
Failure Time Data ◽  
Time Data ◽  
Hazard Functions ◽  
Competing Risks Data ◽  
Causes Of Failure

In this paper, we consider joint modeling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint. Hence, we can fit a cause-specific hazards submodel to allow for competing risks, with a separate latent association between longitudinal measurements and each cause of failure. We also consider the possible masked causes of failure in joint modeling of repeated measurements and competing risks failure time data. We also derive a score test to identify longitudinal biomarkers or surrogates for a time-to-event outcome in competing risks data which contain masked causes of failure. With a carefully chosen definition of complete data, the maximum likelihood estimation of the cause-specific hazard functions and of the masking probabilities is performed via an expectation maximization algorithm. The simulations are used to explore how the number of individuals, the number of time points per individual, and the functional form of the random effects from the longitudinal biomarkers considering heterogeneous baseline hazards in individuals influence the power to detect the association of a longitudinal biomarker and the survival time.

scholarly journals Joint modelling of longitudinal and survival data in the presence of competing risks with applications to prostate cancer data

2020 ◽  
pp. 1471082X2094462
Author(s):  
Md. Tuhin Sheikh ◽  
Joseph G. Ibrahim ◽  
Jonathan A. Gelfond ◽  
Wei Sun ◽  
Ming-Hui Chen
Keyword(s):  
Prostate Cancer ◽  
Competing Risks ◽  
Survival Data ◽  
Monte Carlo Sampling ◽  
Low Grade ◽  
High Grade ◽  
Cancer Data ◽  
Empirical Performance ◽  
Competing Causes ◽  
Causes Of Failure

This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer or the time to high-grade cancer (competing risks). In this article, the goal is to model the longitudinal PSA data and the time-to-prostate cancer (PC) due to low- or high-grade. We consider the low-grade and high-grade as two competing causes of developing PC. A joint model for simultaneously analysing longitudinal and time-to-event data in the presence of multiple causes of failure (or competing risk) is proposed within the Bayesian framework. The proposed model allows for handling the missing causes of failure in the SELECT data and implementing an efficient Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via a novel reparameterization technique. Bayesian criteria, [Formula: see text]DIC[Formula: see text], and [Formula: see text]WAIC[Formula: see text], are introduced to quantify the gain in fit in the survival sub-model due to the inclusion of longitudinal data. A simulation study is conducted to examine the empirical performance of the posterior estimates as well as [Formula: see text]DIC[Formula: see text] and [Formula: see text]WAIC[Formula: see text] and a detailed analysis of the SELECT data is also carried out to further demonstrate the proposed methodology.

scholarly journals Bayesian Inference of the Weibull Model Based on Interval-Censored Survival Data

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Chris Bambey Guure ◽  
Noor Akma Ibrahim ◽  
Mohd Bakri Adam
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Survival Data ◽  
Failure Time ◽  
Real Data ◽  
Weibull Model ◽  
Bayesian Estimator ◽  
Interval Censored Data ◽  
Weibull Parameters ◽  
Interval Censored

Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.

scholarly journals Model evaluation and variable selection for interval-censored data

2015 ◽  
Author(s):  
◽  
Tyler Cook
Keyword(s):  
Survival Analysis ◽  
Variable Selection ◽  
Survival Time ◽  
Censored Data ◽  
Survival Data ◽  
Failure Time ◽  
Dependent Censoring ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Selection For

Survival analysis is a popular area of statistics dealing with time-to-event data. A special characteristic of survival data is the presence of censoring. Censoring occurs when the survival time is only partially known. In medical studies, censoring can be caused by patients dropping out of the study before their disease event occurs. This dissertation focuses on the analysis of interval-censored data, where the failure time is only known to belong to some interval of observation times. One problem researchers face when analyzing survival data is how to handle the censoring distribution. This is an important consideration because sometimes a patient's survival time is related to the time they drop out of the study. It is often assumed that these two times are unrelated, so special methods need to be developed when they are dependent. Part of this dissertation investigates the effectiveness of methods developed for interval-censored data with dependent censoring when the censoring is actually independent. The results of these simulation studies can provide guidelines for deciding between models when facing a practical problem where one is unsure about the dependence of the censoring distribution. Another important problem seen in survival analysis is variable selection. For example, doctors might want to identify a set of diagnostic tests or measurements that can predict patient survival. We propose an imputation approach for variable selection of interval-censored data that utilizes penalized likelihood procedures. This work is significant because researchers currently do not have many tools to select important variables related to the survival time for interval-censored data.

scholarly journals Competing Hazards Regression Parameter Estimation Under Different Informative Priors

2021 ◽  
Author(s):  
H Rehman ◽  
Navin Chandra ◽  
Fatemeh Sadat Hosseini-Baharanchi ◽  
Ahmad Reza Baghestani
Keyword(s):  
Survival Data ◽  
Hazard Function ◽  
Failure Time ◽  
Marrow Transplant ◽  
Point Of View ◽  
Informative Priors ◽  
Baseline Model ◽  
Hazard Functions ◽  
Parametric Regression ◽  
Latent Failure

In the analysis of survival data, cause specific quantities of competing risks get considerable attention as compared to latent failure time approach. This article focuses on parametric regression analysis of survival data using cause specific hazard function with Burr type XII distribution as a baseline model. We obtained maximum likelihood and Bayes estimates of cumulative cause specific hazard functions under competing risk setup. For Bayesian point of view we proposed a class of informative priors for parameters to observe the comprehensive compatibility and their effectiveness under two different loss functions. The appropriateness of model is measured by the simulation study. Finally, we illustrate the proposed methodologies using bone marrow transplant data from the Princess Margaret Hospital Ontario, Canada.

scholarly journals Bayesian Estimations of Exponential Distribution Based on Interval-Censored Data with a Cure Fraction

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Al Omari Mohammed Ahmed
Keyword(s):  
Exponential Distribution ◽  
Censored Data ◽  
Bayesian Approach ◽  
Failure Time ◽  
Upper And Lower Bounds ◽  
Interval Censored Data ◽  
Squared Error ◽  
Cure Fraction ◽  
Interval Censored ◽  
The Bayesian Approach

Censored data are considered to be of the interval type where the upper and lower bounds of an event’s failure time cannot be directly observed but only determined between interval inspection times. The analyses of interval-censored data have attracted attention because they are common in the fields of reliability and medicine. A proportion of patients enrolled in clinical trials can sometimes be cured. In some instances, their symptoms mostly disappear without any recurrence of the disease. In this study, the proportion of such patients who are cured is estimated. Furthermore, the Bayesian approach under the gamma prior and maximum likelihood estimation (MLE) is used to estimate the cure fraction depending on the bounded cumulative hazard (BCH) model based on interval-censored data with an exponential distribution. The Bayesian approach uses three loss functions: squared error, linear exponential, and general entropy. These functions are compared with the MLE and used between estimators. Moreover, they are obtained using the mean squared error, which locates the best option to estimate the parameter of an exponential distribution. The results show that the BCH model and lambda parameter of the exponential distribution based on the interval-censored data can be best estimated using the Bayesian gamma prior with a positive loss function of the linear exponential.

scholarly journals Dipolar Tree Ensemble With and Without Adjustment to Competing Risks: Application to Medical Data

2013 ◽  
Vol 35 (1) ◽  
pp. 27-38
Author(s):  
Małgorzata Krętowska
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Time Distribution ◽  
Failure Time ◽  
Medical Data ◽  
Competing Risk ◽  
Failure Time Distribution

Abstract The analysis of survival data often aims at the prediction of failure time distribution. In cases of competing risk events, the time distributions of more than one failure are under investigation. In this paper, the comparison of two approaches to analyzing survival data with competing risks is presented. The analyses are performed by use of an ensemble of dipolar trees with and without adjustment to competing risks.

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